Introduction
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In this project we seek to move away from the traditional paradigm for 2D object recognition whereby objects are identified in the image as 2D bounding boxes. We focus instead on: i) detecting objects; ii) identifying their 3D poses; iii) characterizing the geometrical and topological properties of the objects in terms of their aspect configurations in 3D. We call such characterization an object's aspect layout. We propose a new model for solving these problems in a joint fashion from a single image for object categories. Our model is constructed upon a novel framework based on conditional random fields with maximal margin parameter estimation. Extensive experiments are conducted to evaluate our model's performance in determining object pose and layout from images. We achieve superior viewpoint accuracy results on three public datasets and show extensive quantitative analysis to demonstrate the ability of accurately recovering the aspect layout of objects.
Dataset and Code
- The source code and the datasets in our experiments can be found here (~ 660M, including the new ImageNet data we constructed).
Acknowledgements
- We acknowledge the support of ARO under grant W911NF-09-1-0310 and NSF CAREER under grant #1054127.
References
- S. Savarese and L. Fei-Fei. 3d generic object categorization, localization and pose estimation. In International Conference on Computer Vision (ICCV), 2007.
- http://www.image-net.org.
Contact : yuxiang at umich dot edu
Last update : 7/27/2012
